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Sensors in Aircraft (Volume II)
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Sensors in Aircraft (Volume II)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 4106

Special Issue Editor

Prof. Dr. Jaroslaw Alexander Pytka

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
Interests: aerospace and automotive engineering; metrology; sensors and sensors systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue, “Sensors in Aircraft”.

Modern aircraft are complex technical objects with many different sensors and measuring systems, e.g., on-board instruments, navigation, guidance and control systems, propulsion engines and fuel systems, and hydraulic and passenger safety systems, which operate based on data signals obtained using various sensors. The trend to minimize the human factor is seen in, among others, the use of automatic aircraft guidance systems, which in turn are based on sensor systems. This is especially true for unmanned aerial vehicles, in which vision sensor systems partly play the role of a pilot. Furthermore, in the design, research, and development process of aircraft, sensors and measuring systems play an important role, providing necessary data on a regular basis. This Special Issue of Sensors is devoted to innovations, novel solutions, and new research in the field of physical sensors in aircraft. Original contributions referring to the following topics are welcome:

  • Physical sensors and sensor systems in aircraft navigation and on-board diagnostic systems;
  • Instruments based on physical sensors;
  • Sensors and instrumentation for the flight testing of aircrafts;
  • Sensors in measurement technology for design, research, and development, as well as production engineering of aircrafts;
  • Smart sensors based on fuzzy logic and artificial intelligence methods and technology;
  • Aircrafts sensors and sensing systems based on the Internet of Things;
  • Aircraft as a sensor;
  • Optical sensors for unmanned aerial vehicles;
  • Sensors for monitoring the pilot’s environment in aircraft, for example, onboard electromagnetic field sensors;
  • Other aspects of sensors and sensing technology for aircraft and aerospace.

Research contributions (both experimental and analytical), design studies, literature reviews, as well as applications and technology demonstrators are welcome.

Prof. Dr. Jaroslaw Alexander Pytka
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (1 paper)

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Research

29 pages, 1651 KiB  
Article
Quaternion-Based Attitude Estimation of an Aircraft Model Using Computer Vision
by Pavithra Kasula, James F. Whidborne and Zeeshan A. Rana
Sensors 2024, 24(12), 3795; https://doi.org/10.3390/s24123795 - 12 Jun 2024
Viewed by 3665
Abstract
Investigating aircraft flight dynamics often requires dynamic wind tunnel testing. This paper proposes a non-contact, off-board instrumentation method using vision-based techniques. The method utilises a sequential process of Harris corner detection, Kanade–Lucas–Tomasi tracking, and quaternions to identify the Euler angles from a pair [...] Read more.
Investigating aircraft flight dynamics often requires dynamic wind tunnel testing. This paper proposes a non-contact, off-board instrumentation method using vision-based techniques. The method utilises a sequential process of Harris corner detection, Kanade–Lucas–Tomasi tracking, and quaternions to identify the Euler angles from a pair of cameras, one with a side view and the other with a top view. The method validation involves simulating a 3D CAD model for rotational motion with a single degree-of-freedom. The numerical analysis quantifies the results, while the proposed approach is analysed analytically. This approach results in a 45.41% enhancement in accuracy over an earlier direction cosine matrix method. Specifically, the quaternion-based method achieves root mean square errors of 0.0101 rad/s, 0.0361 rad/s, and 0.0036 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. Notably, the method exhibits a 98.08% accuracy for the pitch rate. These results highlight the performance of quaternion-based attitude estimation in dynamic wind tunnel testing. Furthermore, an extended Kalman filter is applied to integrate the generated on-board instrumentation data (inertial measurement unit, potentiometer gimbal) and the results of the proposed vision-based method. The extended Kalman filter state estimation achieves root mean square errors of 0.0090 rad/s, 0.0262 rad/s, and 0.0034 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. This method exhibits an improved accuracy of 98.61% for the estimation of pitch rate, indicating its higher efficiency over the standalone implementation of the direction cosine method for dynamic wind tunnel testing. Full article
(This article belongs to the Special Issue Sensors in Aircraft (Volume II))
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